docs: def graph_from_labels(label_image,
                          fg_markers,
                          bg_markers,
                          regional_term = False,
                          boundary_term = False,
                          regional_term_args = False,
                          boundary_term_args = False):
      """
      Create a graph-cut ready graph to segment a nD image using the region neighbourhood.
      
      Create a `~medpy.graphcut.maxflow.GraphDouble` object for all regions of a nD label
      image.
      
      Every region of the label image is regarded as a node. They are connected to their
      immediate neighbours by arcs. If to regions are neighbours is determined using
      :math:`ndim*2`-connectedness (e.g. :math:`3*2=6` for 3D).
      In the next step the arcs weights (n-weights) are computed using the supplied
      ``boundary_term`` function (see :mod:`~medpy.graphcut.energy_voxel` for a selection).
      
      Implicitly the graph holds two additional nodes: the source and the sink, so called
      terminal nodes. These are connected with all other nodes through arcs of an initial
      weight (t-weight) of zero.
      All regions that are under the foreground markers are considered to be tightly bound
      to the source: The t-weight of the arc from source to these nodes is set to a maximum 
      value. The same goes for the background markers: The covered regions receive a
      maximum (`~medpy.graphcut.graph.GCGraph.MAX`) t-weight for their arc towards the sink.
      
      All other t-weights are set using the supplied ``regional_term`` function
      (see :mod:`~medpy.graphcut.energy_voxel` for a selection).
      
      Parameters
      ----------
      label_image: ndarray
          The label image as an array cwhere each voxel carries the id of the region it
          belongs to. Note that the region labels have to start from 1 and be continuous
          (can be achieved with `~medpy.filter.label.relabel`).
      fg_markers : ndarray
          The foreground markers as binary array of the same shape as the original image.
      bg_markers : ndarray
          The background markers as binary array of the same shape as the original image.
      regional_term : function
          This can be either `False`, in which case all t-weights are set to 0, except for
          the nodes that are directly connected to the source or sink; or a function, in
          which case the supplied function is used to compute the t_edges. It has to
          have the following signature *regional_term(graph, regional_term_args)*, and is
          supposed to compute (source_t_weight, sink_t_weight) for all regions of the image
          and add these to the passed `~medpy.graphcut.graph.GCGraph` object. The weights
          have only to be computed for nodes where they do not equal zero. Additional
          parameters can be passed to the function via the ``regional_term_args`` parameter.
      boundary_term : function
          This can be either `False`, in which case all n-edges, i.e. between all nodes
          that are not source or sink, are set to 0; or a function, in which case the
          supplied function is used to compute the edge weights. It has to have the
          following signature *boundary_term(graph, boundary_term_args)*, and is supposed
          to compute the edges between all adjacent regions of the image and to add them
          to the supplied `~medpy.graphcut.graph.GCGraph` object. Additional parameters
          can be passed to the function via the ``boundary_term_args`` parameter.
      regional_term_args : tuple
          Use this to pass some additional parameters to the ``regional_term`` function.
      boundary_term_args : tuple    
          Use this to pass some additional parameters to the ``boundary_term`` function.
  
      Returns
      -------
      graph : `~medpy.graphcut.maxflow.GraphDouble`
          The created graph, ready to execute the graph-cut.
      
      Raises
      ------
      AttributeError
          If an argument is malformed.
      FunctionError
          If one of the supplied functions returns unexpected results.
      
      Notes
      -----
      If a voxel is marked as both, foreground and background, the background marker
      is given higher priority.
       
      All arcs whose weight is not explicitly set are assumed to carry a weight of zero.    
      """    
      # prepare logger
      logger = Logger.getInstance()
      
      logger.info('Performing attribute tests...')
      
      # check, set and convert all supplied parameters
      label_image = scipy.asarray(label_image)
      fg_markers = scipy.asarray(fg_markers, dtype=scipy.bool_)
      bg_markers = scipy.asarray(bg_markers, dtype=scipy.bool_)
      
      __check_label_image(label_image)
      
      # set dummy functions if not supplied
      if not regional_term: regional_term = __regional_term_label
      if not boundary_term: boundary_term = __boundary_term_label
      
      # check supplied functions and their signature
      if not hasattr(regional_term, '__call__') or not 3 == len(inspect.getargspec(regional_term)[0]):
          raise AttributeError('regional_term has to be a callable object which takes three parameters.')
      if not hasattr(boundary_term, '__call__') or not 3 == len(inspect.getargspec(boundary_term)[0]):
          raise AttributeError('boundary_term has to be a callable object which takes three parameters.')    
      
      logger.info('Determining number of nodes and edges.')
      
      # compute number of nodes and edges
      nodes = len(scipy.unique(label_image))
      # POSSIBILITY 1: guess the number of edges (in the best situation is faster but requires a little bit more memory. In the worst is slower.)
      edges = 10 * nodes
      logger.debug('guessed: #nodes={} nodes / #edges={}'.format(nodes, edges))
      # POSSIBILITY 2: compute the edges (slow)
      #edges = len(__compute_edges(label_image))
      #logger.debug('computed: #nodes={} nodes / #edges={}'.format(nodes, edges))
          
      # prepare result graph
      graph = GCGraph(nodes, edges)
                                          
      logger.debug('#hardwired-nodes source/sink={}/{}'.format(len(scipy.unique(label_image[fg_markers])),
                                                               len(scipy.unique(label_image[bg_markers]))))
                   
      #logger.info('Extracting the regions bounding boxes...')
      # extract the bounding boxes
      #bounding_boxes = find_objects(label_image)
          
      # compute the weights of all edges from the source and to the sink i.e.
      # compute the weights of the t_edges Wt
      logger.info('Computing and adding terminal edge weights...')
      #regions = set(graph.get_nodes()) - set(graph.get_source_nodes()) - set(graph.get_sink_nodes())
      regional_term(graph, label_image, regional_term_args) # bounding boxes indexed from 0 # old version: regional_term(graph, label_image, regions, bounding_boxes, regional_term_args)
  
      # compute the weights of the edges between the neighbouring nodes i.e.
      # compute the weights of the n_edges Wr
      logger.info('Computing and adding inter-node edge weights...')
      boundary_term(graph, label_image, boundary_term_args)
      
      # collect all regions that are under the foreground resp. background markers i.e.
      # collect all nodes that are connected to the source resp. sink
      logger.info('Setting terminal weights for the markers...')
      graph.set_source_nodes(scipy.unique(label_image[fg_markers] - 1)) # requires -1 to adapt to node id system
      graph.set_sink_nodes(scipy.unique(label_image[bg_markers] - 1))
      
      return graph.get_graph()